We address the problem of measuring matching similarity in terms of template matching. A novel method called two-side agreement learning (TAL) is proposed which learns the implicit correlation between two sets of multi-dimensional data points. TAL learns from a matching exemplar to construct a symmetric tree-structured model. Two points from source set and target set agree to form a two-side agreement (TA) pair if each point falls into the same leaf cluster of the model. In the training stage, unsupervised weak hyper-planes of each node are learned at first. After then, tree selection based on a cost function yields final model. In the test stage, points are propagated down to leaf nodes and TA pairs are observed to quantify the similarity. Using TAL can reduce the ambiguity in defining similarity which is hard to be objectively defined and lead to more convergent results. Experiments show the effectiveness against the state-of-the-art methods qualitatively and quantitatively.

The online unit clustering problem is one of the most basic clustering problems proposed by Chan and Zarrabi-Zadeh (WAOA2007 and Theory of Computing Systems 45(3), 2009). Several variants of this problem have been extensively studied. In this letter, we propose a new variant of the online unit clustering problem, called the online unit clustering problem with capacity constraints. For this problem, we use competitive analysis to evaluate the performance of an online algorithm. Then, we develop an online algorithm whose competitive ratio is at most 3.178, and show that a lower bound on the competitive ratio of any online algorithm is 2.

The sparse representation models have been widely applied in image super-resolution. The certain optimization problem is supposed and can be solved by the iterative shrinkage algorithm. During iteration, the update of dictionaries and similar patches is necessary to obtain prior knowledge to better solve such ill-conditioned problem as image super-resolution. However, both the processes of iteration and update often spend a lot of time, which will be a bottleneck in practice. To solve it, in this paper, we present the concept of image quality difference based on generalized Gaussian distribution feature which has the same trend with the variation of Peak Signal to Noise Ratio (PSNR), and we update dictionaries or similar patches from the termination strategy according to the adaptive threshold of the image quality difference. Based on this point, we present two sparse representation algorithms for image super-resolution, one achieves the further improvement in image quality and the other decreases running time on the basis of image quality assurance. Experimental results also show that our quantitative results on several test datasets are in line with exceptions.

Correlation filter-based approaches achieve competitive results in visual tracking, but the traditional correlation tracking methods failed in mining the color information of the videos. To address this issue, we propose a novel tracker combined with color features in a correlation filter framework, which extracts not only gray but also color information as the feature maps to compute the maximum response location via multi-channel correlation filters. In particular, we modify the label function of the conventional classifier to improve positioning accuracy and employ a discriminative correlation filter to handle scale variations. Experiments are performed on 35 challenging benchmark color sequences. And the results clearly show that our method outperforms state-of-the-art tracking approaches while operating in real-time.

In this paper, we investigate the problem of maximizing the weighted sum outage rate in multiuser multiple-input single-output (MISO) interference channels, where the transmitters have no knowledge of the exact values of channel coefficients, only the statistical information. Unfortunately, this problem is nonconvex and very difficult to deal with. We propose a new, provably convergent iterative algorithm where in each iteration, the original problem is approximated as second-order cone programming (SOCP) by introducing slack variables and using convex approximation. Simulation results show that the proposed SOCP algorithm converges in a few steps, and yields a better performance gain with a lower computational complexity than existing algorithms.

We propose a new configuration for phased array antennas. The proposal uses radiation pattern reconfigurable antennas as the antenna element to improve the gain on the scanning angle and to suppress the grating lobes of sparse phased array antennas. This configuration can reduce the element number because the desired gain of the total array can be achieved by using fewer elements. We demonstrate the concept by designing a radiation pattern reconfigurable Yagi-Uda antenna. PIN diode switches are added to the parasitic elements to change director and reflector. The switches of multiple array elements are concurrently controlled by just a single one-pair line. This control structure is simple and can be applied to large-scale arrays. The proposed antenna yields an element gain that almost matches the theoretical limit across about half the coverage, even if the element spacing is enlarged to 1λ. If the switch states are interchanged, the gain in the mirror direction can be increased. We design a 48-element array and compare its gain against those of normal dipole antennas. We also fabricate the proposed antenna and demonstrate radiation pattern switching.

Case studies have reported that pedestrian detection methods using vehicle radar are not complete systems because each system has specific limitations at the cost of the calculating amounts, the system complexity or the range resolution. In this letter, we proposed a novel pedestrian detection method by template matching using Gabor filter bank, which was evaluated based on the data observed by 24GHz UWB radar.

Due to outsourcing of numerous stages of the IC manufacturing process to different foundries, the security risk, such as hardware Trojan becomes a potential threat. In this paper, we present a layout aware localized hardware Trojan detection method that magnifies the detection sensitivity for small Trojan in power-based side-channel analysis. A scan segmentation approach with a modified launch-on-capture (LoC) transition delay fault test pattern application technique is proposed so as to maximize the dynamic power consumption of any target region. The new architecture allows activating any target region and keeping others quiet, which reduces total circuit toggling activity. We evaluate our approach on ISCAS89 benchmark and two practical circuits to demonstrate its effectiveness in side-channel analysis.

We study a use of Gaussian kernels with a wide range of scales for nonlinear function estimation. The estimation task can then be split into two sub-tasks: (i) model selection and (ii) learning (parameter estimation) under the selected model. We propose a fully-adaptive and all-in-one scheme that jointly carries out the two sub-tasks based on the multikernel adaptive filtering framework. The task is cast as an asymptotic minimization problem of an instantaneous fidelity function penalized by two types of block l1-norm regularizers. Those regularizers enhance the sparsity of the solution in two different block structures, leading to efficient model selection and dictionary refinement. The adaptive generalized forward-backward splitting method is derived to deal with the asymptotic minimization problem. Numerical examples show that the scheme achieves the model selection and learning simultaneously, and demonstrate its striking advantages over the multiple kernel learning (MKL) method called SimpleMKL.

One of the major subjects for marine resources development and information processing is how to realize underwater short-range and large-capacity data transmissions. The acoustic wave is an effective carrier and has been used for underwater data transmissions because it has lower attenuation in seawater than the radio wave, and has average propagation distance of about 10km or more. However, along with the imaging of transmission data, the inherent low speed of the acoustic wave makes it cannot and become an ideal carrier for high-speed and large-capacity communications. On the other hand, visible-light wave with wavelength of 400nm-650nm is an ideal carrier, which has received much attention. Its attractive features are high transparency and low attenuation rate in underwater, easily control the propagation direction and range by the visibility, and high data rate and capacity, making it excellent for application in underwater wireless communications. However, visible-light waves in the seawater have the spectral attenuation characteristics due to different marine environment. Therefore, in this paper an underwater optical wireless communication method with adaptation seawater function is considered for seawater turbidity of the spatio-temporal change. Two crucial components in the underwater optical wireless communication system, the light wavelength and the modulation method are controlled using wavelength- and modulation-adaptation techniques, respectively. The effectiveness of the method of the adaptation wavelength is demonstrated in underwater optical image transmissions.

After the East Japan great earthquake on March 11, 2011, many Japanese coastal resident areas were isolated from other because of destruction of information infrastructure, disconnection of communication network and excessive traffic congestion. The undelivered disaster information influenced the speed of evacuation, rescue of injured residents, and sending life-support materials to evacuation shelters. From the experience of such disaster, more robust and resilient networks are strongly required, particularly for preparation of large scale disasters. In this paper, in order to respond to those problems, we introduce Delay Tolerant Network (DTN) for disaster information transmission application in challenged network environment. Message delivery by transport vehicles such as cars between disaster-response headquarter and evacuation shelters in challenged network environment is considered. A improved message delivery method combined with DTN protocols and cognitive wireless network is explained. The computer simulation for the actual rural area in Japan is made to evaluate the performance and effectiveness of proposed method.

Object detection is the first step in the object recognition. According to the detection results, its following works are affected. However, object detection has a heavy resource requirement in terms of, computing power and memory. If an image is enlarged, the computational load required for object detection is also increased. An-integral-image-based method guarantees fast object detection. Once an integral image is generated, the speed of the object detection procedure remains fixed, regardless of the pattern region size. However, this becomes an even greater issue if the image is enlarged. In this paper, we propose the use of directional integral image based object detection. A directional integral image gives direction to an integral image, which can then be calculated from various directions. Furthermore, many unnecessary calculations, which typically occur when a partial integral image is used for object detection, can be avoided. Therefore, the amount of computation is reduced, compared with methods using integral images. In experiments comparing methods, the proposed method required 40% fewer computations.

Single-instruction multiple-data (SIMD) extension provides an energy-efficient platform to scale the performance of media and scientific applications while still retaining post-programmability. However, the major challenge is to translate the parallel resources of the SIMD hardware into real application performance. Currently, all the slots in the vector register are used when compilers exploit SIMD parallelism of programs, which can be called sufficient vectorization. Sufficient vectorization means all the data in the vector register is valid. Because all the slots which vector register provides must be used, the chances of vectorizing programs with low SIMD parallelism are abandoned by sufficient vectorization method. In addition, the speedup obtained by full use of vector register sometimes is not as great as that obtained by partial use. Specifically, the length of vector register provided by SIMD extension becomes longer, sufficient vectorization method cannot exploit the SIMD parallelism of programs completely. Therefore, insufficient vectorization method is proposed, which refer to partial use of vector register. First, the adaptation scene of insufficient vectorization is analyzed. Second, the methods of computing inter-iteration and intra-iteration SIMD parallelism for loops are put forward. Furthermore, according to the relationship between the parallelism and vector factor a method is established to make the choice of vectorization method, in order to vectorize programs as well as possible. Finally, code generation strategy for insufficient vectorization is presented. Benchmark test results show that insufficient vectorization method vectorized more programs than sufficient vectorization method by 107.5% and the performance achieved by insufficient vectorization method is 12.1% higher than that achieved by sufficient vectorization method.

In this paper, we propose a technique to improve the gain of ultra wide-band (UWB) planar antennas by using low profile reflectors based on frequency selective surfaces (FSS). This technique not only enhances the gain of the planar UWB antennas but also guarantees a constant gain with weak variation across the entire UWB while keeping their attractive merits such as planar structure and easy fabrication. An UWB coplanar waveguide (CPW) fed antenna is installed above the proposed reflectors, to prove the effectiveness of the proposed technique. As a result, a constant gain is achieved across a very large bandwidth.

All the existing sender-based message logging (SBML) protocols share a well-known limitation that they cannot tolerate concurrent failures. In this paper, we analyze the cause for this limitation in a unicast network environment, and present an enhanced SBML protocol to overcome this shortcoming while preserving the strengths of SBML. When the processes on different nodes execute a distributed application together in a broadcast network, this new protocol replicates the log information of each message to volatile storages of other processes within the same broadcast network. It may reduce the communication overhead for the log replication by taking advantage of the broadcast nature of the network. Simulation results show our protocol performs better than the traditional one modified to tolerate concurrent failures in terms of failure-free execution time regardless of distributed application communication pattern.

Many hemodialysis patients undergo plasitc surgery to form the arterio-venous fistula (AVF) in their forearm to improve the vascular access by shunting blood flows. The issue of AVF is the stenosis caused by the disturbance of blood flows; therefore the auscultation system to assist the stenosis diagnosis has been developed. Although the system is intended to be used as a steady monitoring for stenosis assessment, its efficiency was not always high because it cannot estimate where the stenosis locates. In this study, for extracting and estimating the stenosis signal, the shunt murmurs captured by many microphones were decomposed by the principal component analysis (PCA). Furthermore, applying the hierarchical categorization of the recursive subdivision self-organizing map (rs-SOM), the modelling of the stenosis signal was proposed to realise the effective stenosis assessment. The false-positive rate of the stenosis assessment was significantly reduced by using the improved auscultation system.

By installing the various types of cells, imbalance in traffic load and excessive handover among cells in a heterogenous network can be prevalent. To deal with this problem, we propose a mobility-based cell association algorithm for load balancing in a heterogenous network. By defining a dynamic system load as a function of the mobility of mobile stations (MSs) and the transmit powers of cells, the proposed algorithm is designed such that it can optimize a utility function based on the fairness of the dynamic system load. Simulation results verify that the proposed algorithm improves the user perceived rate of MSs located at cell edges with slight increase in the number of handovers compared to a conventional cell association based on received signal strength.

Research on intrusion-tolerant systems (ITSs) is being conducted to protect critical systems which provide useful information services. To provide services reliably, these critical systems must not have even a single point of failure (SPOF). Therefore, most ITSs employ redundant components to eliminate the SPOF problem and improve system reliability. However, systems that include identical components have common vulnerabilities that can be exploited to attack the servers. Attackers prefer to exploit these common vulnerabilities rather than general vulnerabilities because the former might provide an opportunity to compromise several servers. In this study, we analyze software vulnerability data from the National Vulnerability Database (NVD). Based on the analysis results, we present a scheme that finds software combinations that minimize the risk of common vulnerabilities. We implement this scheme with CSIM20, and simulation results prove that the proposed scheme is appropriate for a recovery-based intrusion tolerant architecture.

This paper investigates the data persistence problem with compressive sensing (CS) in wireless sensor networks (WSNs) where the sensed readings should be temporarily stored among the entire network in a distributed manner until gathered by a mobile sink. Since there is an energy-performance tradeoff, conventional CS-based schemes only focus on reducing the energy consumption or improving the CS construction performance. In this paper, we propose an efficient Compressive Sensing based Data Persistence (CSDP) scheme to achieve the optimum balance between energy consumption and reconstruction performance. Unlike most existing CS-based schemes which require packets visiting the entire network to reach the equilibrium distribution, in our proposed scheme information exchange is only performed among neighboring nodes. Therefore, such an approach will result in a non-uniform distribution of measurements, and the CS measurement matrix depends heavily on the node degree. The CS reconstruction performance and energy consumption are analyzed. Simulation results confirm that the proposed CSDP scheme consumes the least energy and computational overheads compared with other representative schemes, while almost without sacrificing the CS reconstruction performance.

We introduce two cheater identifiable secret sharing (CISS) schemes with efficient reconstruction, tolerating t